Overview

Dataset statistics

Number of variables71
Number of observations1997
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory576.0 B

Variable types

Numeric14
Categorical56
DateTime1

Alerts

moduleid has constant value ""Constant
packet_date_time has constant value ""Constant
i19 has constant value ""Constant
i20 has constant value ""Constant
i21 has constant value ""Constant
i22 has constant value ""Constant
i23 has constant value ""Constant
i24 has constant value ""Constant
i25 has constant value ""Constant
i26 has constant value ""Constant
i27 has constant value ""Constant
i28 has constant value ""Constant
i29 has constant value ""Constant
i30 has constant value ""Constant
i31 has constant value ""Constant
i32 has constant value ""Constant
pf17 has constant value ""Constant
pf19 has constant value ""Constant
pf20 has constant value ""Constant
pf21 has constant value ""Constant
pf22 has constant value ""Constant
pf23 has constant value ""Constant
pf24 has constant value ""Constant
pf25 has constant value ""Constant
pf26 has constant value ""Constant
pf27 has constant value ""Constant
pf28 has constant value ""Constant
pf29 has constant value ""Constant
pf30 has constant value ""Constant
pf31 has constant value ""Constant
pf32 has constant value ""Constant
v_red is highly overall correlated with i6High correlation
v_blue is highly overall correlated with v_yellow and 1 other fieldsHigh correlation
v_yellow is highly overall correlated with v_blue and 1 other fieldsHigh correlation
i1 is highly overall correlated with pf1 and 5 other fieldsHigh correlation
i6 is highly overall correlated with v_red and 1 other fieldsHigh correlation
pf1 is highly overall correlated with i1 and 5 other fieldsHigh correlation
i3 is highly overall correlated with i4 and 2 other fieldsHigh correlation
i4 is highly overall correlated with i3 and 6 other fieldsHigh correlation
i5 is highly overall correlated with i6 and 1 other fieldsHigh correlation
i7 is highly overall correlated with i4 and 6 other fieldsHigh correlation
i8 is highly overall correlated with i3 and 7 other fieldsHigh correlation
i9 is highly overall correlated with i7 and 5 other fieldsHigh correlation
i10 is highly overall correlated with i4 and 4 other fieldsHigh correlation
i11 is highly overall correlated with i1 and 5 other fieldsHigh correlation
i12 is highly overall correlated with i1 and 6 other fieldsHigh correlation
i13 is highly overall correlated with i1 and 7 other fieldsHigh correlation
i14 is highly overall correlated with i1 and 6 other fieldsHigh correlation
i15 is highly overall correlated with pf1 and 10 other fieldsHigh correlation
i16 is highly overall correlated with i7 and 7 other fieldsHigh correlation
i17 is highly overall correlated with i15High correlation
i18 is highly overall correlated with i1 and 12 other fieldsHigh correlation
pf11 is highly overall correlated with v_blue and 1 other fieldsHigh correlation
i9 is highly imbalanced (62.0%)Imbalance
i11 is highly imbalanced (75.3%)Imbalance
i13 is highly imbalanced (60.8%)Imbalance
i14 is highly imbalanced (51.5%)Imbalance
pf3 is highly imbalanced (98.9%)Imbalance
pf5 is highly imbalanced (98.9%)Imbalance
pf6 is highly imbalanced (89.4%)Imbalance
pf8 is highly imbalanced (99.4%)Imbalance
pf9 is highly imbalanced (95.1%)Imbalance
pf11 is highly imbalanced (99.4%)Imbalance
pf14 is highly imbalanced (60.8%)Imbalance
pf15 is highly imbalanced (93.6%)Imbalance
pf18 is highly imbalanced (97.9%)Imbalance
pf13 is highly skewed (γ1 = 20.63945177)Skewed
id has unique valuesUnique
server_date_time has unique valuesUnique
v_blue has 165 (8.3%) zerosZeros
v_yellow has 165 (8.3%) zerosZeros

Reproduction

Analysis started2023-11-28 10:05:46.970250
Analysis finished2023-11-28 10:06:57.253389
Duration1 minute and 10.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct1997
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178244.71
Minimum169155
Maximum193194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2023-11-28T10:06:57.538291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum169155
5-th percentile170558.4
Q1174811
median176377
Q3182570
95-th percentile189728
Maximum193194
Range24039
Interquartile range (IQR)7759

Descriptive statistics

Standard deviation5713.701
Coefficient of variation (CV)0.032055374
Kurtosis-0.29308718
Mean178244.71
Median Absolute Deviation (MAD)3294
Skewness0.69420761
Sum3.5595469 × 108
Variance32646379
MonotonicityNot monotonic
2023-11-28T10:06:58.100367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170496 1
 
0.1%
183212 1
 
0.1%
171183 1
 
0.1%
178094 1
 
0.1%
175278 1
 
0.1%
172206 1
 
0.1%
181165 1
 
0.1%
180397 1
 
0.1%
176301 1
 
0.1%
176045 1
 
0.1%
Other values (1987) 1987
99.5%
ValueCountFrequency (%)
169155 1
0.1%
169161 1
0.1%
169166 1
0.1%
169171 1
0.1%
169176 1
0.1%
169181 1
0.1%
169186 1
0.1%
169190 1
0.1%
169195 1
0.1%
169200 1
0.1%
ValueCountFrequency (%)
193194 1
0.1%
193186 1
0.1%
193182 1
0.1%
193180 1
0.1%
193175 1
0.1%
193173 1
0.1%
193170 1
0.1%
193166 1
0.1%
193163 1
0.1%
193159 1
0.1%

moduleid
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
LBTR07DB01
1997 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters19970
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLBTR07DB01
2nd rowLBTR07DB01
3rd rowLBTR07DB01
4th rowLBTR07DB01
5th rowLBTR07DB01

Common Values

ValueCountFrequency (%)
LBTR07DB01 1997
100.0%

Length

2023-11-28T10:06:58.563007image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:06:58.777542image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
lbtr07db01 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
B 3994
20.0%
0 3994
20.0%
L 1997
10.0%
T 1997
10.0%
R 1997
10.0%
7 1997
10.0%
D 1997
10.0%
1 1997
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11982
60.0%
Decimal Number 7988
40.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 3994
33.3%
L 1997
16.7%
T 1997
16.7%
R 1997
16.7%
D 1997
16.7%
Decimal Number
ValueCountFrequency (%)
0 3994
50.0%
7 1997
25.0%
1 1997
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11982
60.0%
Common 7988
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 3994
33.3%
L 1997
16.7%
T 1997
16.7%
R 1997
16.7%
D 1997
16.7%
Common
ValueCountFrequency (%)
0 3994
50.0%
7 1997
25.0%
1 1997
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 3994
20.0%
0 3994
20.0%
L 1997
10.0%
T 1997
10.0%
R 1997
10.0%
7 1997
10.0%
D 1997
10.0%
1 1997
10.0%

packet_date_time
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
11/11/2011 11:11
1997 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters31952
Distinct characters6
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11/11/2011 11:11
2nd row11/11/2011 11:11
3rd row11/11/2011 11:11
4th row11/11/2011 11:11
5th row11/11/2011 11:11

Common Values

ValueCountFrequency (%)
11/11/2011 11:11 1997
100.0%

Length

2023-11-28T10:06:58.988286image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:06:59.195327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
11/11/2011 1997
50.0%
11:11 1997
50.0%

Most occurring characters

ValueCountFrequency (%)
1 19970
62.5%
/ 3994
 
12.5%
2 1997
 
6.2%
0 1997
 
6.2%
1997
 
6.2%
: 1997
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23964
75.0%
Other Punctuation 5991
 
18.8%
Space Separator 1997
 
6.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 19970
83.3%
2 1997
 
8.3%
0 1997
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 3994
66.7%
: 1997
33.3%
Space Separator
ValueCountFrequency (%)
1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 19970
62.5%
/ 3994
 
12.5%
2 1997
 
6.2%
0 1997
 
6.2%
1997
 
6.2%
: 1997
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 19970
62.5%
/ 3994
 
12.5%
2 1997
 
6.2%
0 1997
 
6.2%
1997
 
6.2%
: 1997
 
6.2%

v_red
Real number (ℝ)

HIGH CORRELATION 

Distinct1725
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14435.84
Minimum7349
Maximum21045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2023-11-28T10:06:59.438862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum7349
5-th percentile8465.4
Q110698
median12817
Q319326
95-th percentile20648.4
Maximum21045
Range13696
Interquartile range (IQR)8628

Descriptive statistics

Standard deviation4492.5429
Coefficient of variation (CV)0.31120758
Kurtosis-1.5700661
Mean14435.84
Median Absolute Deviation (MAD)3627
Skewness0.22100383
Sum28828373
Variance20182942
MonotonicityNot monotonic
2023-11-28T10:06:59.747783image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20691 7
 
0.4%
19149 4
 
0.2%
20511 4
 
0.2%
20692 4
 
0.2%
19321 4
 
0.2%
20685 4
 
0.2%
20499 4
 
0.2%
20509 4
 
0.2%
20634 4
 
0.2%
19287 4
 
0.2%
Other values (1715) 1954
97.8%
ValueCountFrequency (%)
7349 1
0.1%
7603 1
0.1%
7611 1
0.1%
7638 1
0.1%
7640 1
0.1%
7652 1
0.1%
7678 1
0.1%
7693 1
0.1%
7695 1
0.1%
7697 2
0.1%
ValueCountFrequency (%)
21045 1
0.1%
20959 1
0.1%
20881 1
0.1%
20820 1
0.1%
20819 1
0.1%
20796 1
0.1%
20785 1
0.1%
20778 1
0.1%
20758 1
0.1%
20757 1
0.1%

v_blue
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4481723
Minimum0
Maximum26
Zeros165
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2023-11-28T10:07:00.037517image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile4
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.2046914
Coefficient of variation (CV)1.5223958
Kurtosis34.415814
Mean1.4481723
Median Absolute Deviation (MAD)0
Skewness5.3564013
Sum2892
Variance4.8606642
MonotonicityNot monotonic
2023-11-28T10:07:00.322470image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 1615
80.9%
0 165
 
8.3%
2 70
 
3.5%
3 28
 
1.4%
4 22
 
1.1%
7 14
 
0.7%
5 14
 
0.7%
9 9
 
0.5%
8 8
 
0.4%
12 8
 
0.4%
Other values (11) 44
 
2.2%
ValueCountFrequency (%)
0 165
 
8.3%
1 1615
80.9%
2 70
 
3.5%
3 28
 
1.4%
4 22
 
1.1%
5 14
 
0.7%
6 7
 
0.4%
7 14
 
0.7%
8 8
 
0.4%
9 9
 
0.5%
ValueCountFrequency (%)
26 1
 
0.1%
22 3
 
0.2%
18 1
 
0.1%
17 1
 
0.1%
16 2
 
0.1%
15 6
0.3%
14 4
0.2%
13 4
0.2%
12 8
0.4%
11 7
0.4%

v_yellow
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4481723
Minimum0
Maximum26
Zeros165
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2023-11-28T10:07:00.576894image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile4
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.2046914
Coefficient of variation (CV)1.5223958
Kurtosis34.415814
Mean1.4481723
Median Absolute Deviation (MAD)0
Skewness5.3564013
Sum2892
Variance4.8606642
MonotonicityNot monotonic
2023-11-28T10:07:00.854521image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 1615
80.9%
0 165
 
8.3%
2 70
 
3.5%
3 28
 
1.4%
4 22
 
1.1%
7 14
 
0.7%
5 14
 
0.7%
9 9
 
0.5%
8 8
 
0.4%
12 8
 
0.4%
Other values (11) 44
 
2.2%
ValueCountFrequency (%)
0 165
 
8.3%
1 1615
80.9%
2 70
 
3.5%
3 28
 
1.4%
4 22
 
1.1%
5 14
 
0.7%
6 7
 
0.4%
7 14
 
0.7%
8 8
 
0.4%
9 9
 
0.5%
ValueCountFrequency (%)
26 1
 
0.1%
22 3
 
0.2%
18 1
 
0.1%
17 1
 
0.1%
16 2
 
0.1%
15 6
0.3%
14 4
0.2%
13 4
0.2%
12 8
0.4%
11 7
0.4%

i1
Real number (ℝ)

HIGH CORRELATION 

Distinct209
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.69354
Minimum35
Maximum912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2023-11-28T10:07:01.378339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile35
Q139
median45
Q363
95-th percentile300.2
Maximum912
Range877
Interquartile range (IQR)24

Descriptive statistics

Standard deviation92.432108
Coefficient of variation (CV)1.145471
Kurtosis11.643311
Mean80.69354
Median Absolute Deviation (MAD)8
Skewness3.0681546
Sum161145
Variance8543.6946
MonotonicityNot monotonic
2023-11-28T10:07:01.922911image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 162
 
8.1%
36 114
 
5.7%
35 104
 
5.2%
40 99
 
5.0%
39 97
 
4.9%
43 92
 
4.6%
41 86
 
4.3%
45 86
 
4.3%
57 80
 
4.0%
44 80
 
4.0%
Other values (199) 997
49.9%
ValueCountFrequency (%)
35 104
5.2%
36 114
5.7%
37 162
8.1%
38 48
 
2.4%
39 97
4.9%
40 99
5.0%
41 86
4.3%
42 60
 
3.0%
43 92
4.6%
44 80
4.0%
ValueCountFrequency (%)
912 1
0.1%
813 1
0.1%
705 1
0.1%
623 1
0.1%
601 1
0.1%
572 1
0.1%
571 1
0.1%
552 1
0.1%
528 1
0.1%
521 1
0.1%

i2
Real number (ℝ)

Distinct727
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean466.8678
Minimum7
Maximum1736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2023-11-28T10:07:02.387341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile159.8
Q1260
median391
Q3646
95-th percentile949
Maximum1736
Range1729
Interquartile range (IQR)386

Descriptive statistics

Standard deviation285.91274
Coefficient of variation (CV)0.6124062
Kurtosis1.017154
Mean466.8678
Median Absolute Deviation (MAD)147
Skewness1.0368749
Sum932335
Variance81746.093
MonotonicityNot monotonic
2023-11-28T10:07:02.711827image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 33
 
1.7%
303 13
 
0.7%
283 12
 
0.6%
291 12
 
0.6%
13 11
 
0.6%
302 11
 
0.6%
431 10
 
0.5%
290 10
 
0.5%
287 10
 
0.5%
286 10
 
0.5%
Other values (717) 1865
93.4%
ValueCountFrequency (%)
7 6
 
0.3%
8 33
1.7%
9 1
 
0.1%
11 6
 
0.3%
12 9
 
0.5%
13 11
 
0.6%
14 1
 
0.1%
96 1
 
0.1%
97 1
 
0.1%
98 1
 
0.1%
ValueCountFrequency (%)
1736 1
0.1%
1722 1
0.1%
1721 1
0.1%
1680 1
0.1%
1479 1
0.1%
1473 1
0.1%
1471 1
0.1%
1467 1
0.1%
1455 1
0.1%
1440 1
0.1%

i3
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
10
1147 
9
706 
8
127 
11
 
17

Length

Max length2
Median length2
Mean length1.5828743
Min length1

Characters and Unicode

Total characters3161
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row9
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10 1147
57.4%
9 706
35.4%
8 127
 
6.4%
11 17
 
0.9%

Length

2023-11-28T10:07:03.024882image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:03.357466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
10 1147
57.4%
9 706
35.4%
8 127
 
6.4%
11 17
 
0.9%

Most occurring characters

ValueCountFrequency (%)
1 1181
37.4%
0 1147
36.3%
9 706
22.3%
8 127
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3161
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1181
37.4%
0 1147
36.3%
9 706
22.3%
8 127
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3161
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1181
37.4%
0 1147
36.3%
9 706
22.3%
8 127
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1181
37.4%
0 1147
36.3%
9 706
22.3%
8 127
 
4.0%

i4
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
8
950 
9
938 
7
101 
10
 
8

Length

Max length2
Median length1
Mean length1.004006
Min length1

Characters and Unicode

Total characters2005
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row8
3rd row9
4th row9
5th row9

Common Values

ValueCountFrequency (%)
8 950
47.6%
9 938
47.0%
7 101
 
5.1%
10 8
 
0.4%

Length

2023-11-28T10:07:03.843704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:04.262177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
8 950
47.6%
9 938
47.0%
7 101
 
5.1%
10 8
 
0.4%

Most occurring characters

ValueCountFrequency (%)
8 950
47.4%
9 938
46.8%
7 101
 
5.0%
1 8
 
0.4%
0 8
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2005
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 950
47.4%
9 938
46.8%
7 101
 
5.0%
1 8
 
0.4%
0 8
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2005
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 950
47.4%
9 938
46.8%
7 101
 
5.0%
1 8
 
0.4%
0 8
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 950
47.4%
9 938
46.8%
7 101
 
5.0%
1 8
 
0.4%
0 8
 
0.4%

i5
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
8
1343 
7
606 
6
 
36
9
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 1343
67.3%
7 606
30.3%
6 36
 
1.8%
9 12
 
0.6%

Length

2023-11-28T10:07:04.731902image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:05.135327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
8 1343
67.3%
7 606
30.3%
6 36
 
1.8%
9 12
 
0.6%

Most occurring characters

ValueCountFrequency (%)
8 1343
67.3%
7 606
30.3%
6 36
 
1.8%
9 12
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 1343
67.3%
7 606
30.3%
6 36
 
1.8%
9 12
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 1343
67.3%
7 606
30.3%
6 36
 
1.8%
9 12
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 1343
67.3%
7 606
30.3%
6 36
 
1.8%
9 12
 
0.6%

i6
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1096645
Minimum6
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2023-11-28T10:07:05.561967image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7
Q17
median8
Q39
95-th percentile10
Maximum11
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.96765927
Coefficient of variation (CV)0.11932174
Kurtosis-0.48757357
Mean8.1096645
Median Absolute Deviation (MAD)1
Skewness0.31077332
Sum16195
Variance0.93636447
MonotonicityNot monotonic
2023-11-28T10:07:06.012506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 784
39.3%
7 537
26.9%
9 456
22.8%
10 180
 
9.0%
6 36
 
1.8%
11 4
 
0.2%
ValueCountFrequency (%)
6 36
 
1.8%
7 537
26.9%
8 784
39.3%
9 456
22.8%
10 180
 
9.0%
11 4
 
0.2%
ValueCountFrequency (%)
11 4
 
0.2%
10 180
 
9.0%
9 456
22.8%
8 784
39.3%
7 537
26.9%
6 36
 
1.8%

i7
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
7
1339 
6
551 
5
 
68
8
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row7
3rd row7
4th row7
5th row7

Common Values

ValueCountFrequency (%)
7 1339
67.1%
6 551
27.6%
5 68
 
3.4%
8 39
 
2.0%

Length

2023-11-28T10:07:06.509213image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:06.908843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
7 1339
67.1%
6 551
27.6%
5 68
 
3.4%
8 39
 
2.0%

Most occurring characters

ValueCountFrequency (%)
7 1339
67.1%
6 551
27.6%
5 68
 
3.4%
8 39
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 1339
67.1%
6 551
27.6%
5 68
 
3.4%
8 39
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 1339
67.1%
6 551
27.6%
5 68
 
3.4%
8 39
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 1339
67.1%
6 551
27.6%
5 68
 
3.4%
8 39
 
2.0%

i8
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
6
1498 
7
253 
5
246 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row6
3rd row7
4th row7
5th row7

Common Values

ValueCountFrequency (%)
6 1498
75.0%
7 253
 
12.7%
5 246
 
12.3%

Length

2023-11-28T10:07:07.386336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:07.786679image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
6 1498
75.0%
7 253
 
12.7%
5 246
 
12.3%

Most occurring characters

ValueCountFrequency (%)
6 1498
75.0%
7 253
 
12.7%
5 246
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 1498
75.0%
7 253
 
12.7%
5 246
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 1498
75.0%
7 253
 
12.7%
5 246
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 1498
75.0%
7 253
 
12.7%
5 246
 
12.3%

i9
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
7
1650 
6
306 
8
 
40
9
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row6
2nd row7
3rd row7
4th row7
5th row7

Common Values

ValueCountFrequency (%)
7 1650
82.6%
6 306
 
15.3%
8 40
 
2.0%
9 1
 
0.1%

Length

2023-11-28T10:07:08.247931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:08.662766image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
7 1650
82.6%
6 306
 
15.3%
8 40
 
2.0%
9 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
7 1650
82.6%
6 306
 
15.3%
8 40
 
2.0%
9 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 1650
82.6%
6 306
 
15.3%
8 40
 
2.0%
9 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 1650
82.6%
6 306
 
15.3%
8 40
 
2.0%
9 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 1650
82.6%
6 306
 
15.3%
8 40
 
2.0%
9 1
 
0.1%

i10
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
8
927 
9
860 
7
203 
10
 
5
6
 
2

Length

Max length2
Median length1
Mean length1.0025038
Min length1

Characters and Unicode

Total characters2002
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row9
3rd row9
4th row9
5th row9

Common Values

ValueCountFrequency (%)
8 927
46.4%
9 860
43.1%
7 203
 
10.2%
10 5
 
0.3%
6 2
 
0.1%

Length

2023-11-28T10:07:09.164852image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:09.604073image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
8 927
46.4%
9 860
43.1%
7 203
 
10.2%
10 5
 
0.3%
6 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 927
46.3%
9 860
43.0%
7 203
 
10.1%
1 5
 
0.2%
0 5
 
0.2%
6 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2002
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 927
46.3%
9 860
43.0%
7 203
 
10.1%
1 5
 
0.2%
0 5
 
0.2%
6 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2002
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 927
46.3%
9 860
43.0%
7 203
 
10.1%
1 5
 
0.2%
0 5
 
0.2%
6 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2002
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 927
46.3%
9 860
43.0%
7 203
 
10.1%
1 5
 
0.2%
0 5
 
0.2%
6 2
 
0.1%

i11
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
3
1915 
4
 
82

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1915
95.9%
4 82
 
4.1%

Length

2023-11-28T10:07:10.097784image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:10.494900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
3 1915
95.9%
4 82
 
4.1%

Most occurring characters

ValueCountFrequency (%)
3 1915
95.9%
4 82
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1915
95.9%
4 82
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1915
95.9%
4 82
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1915
95.9%
4 82
 
4.1%

i12
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
1
1529 
2
466 
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1529
76.6%
2 466
 
23.3%
3 2
 
0.1%

Length

2023-11-28T10:07:10.922829image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:11.322155image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1529
76.6%
2 466
 
23.3%
3 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 1529
76.6%
2 466
 
23.3%
3 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1529
76.6%
2 466
 
23.3%
3 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1529
76.6%
2 466
 
23.3%
3 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1529
76.6%
2 466
 
23.3%
3 2
 
0.1%

i13
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
5
1843 
6
 
154

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row6
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 1843
92.3%
6 154
 
7.7%

Length

2023-11-28T10:07:11.779549image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:12.169215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
5 1843
92.3%
6 154
 
7.7%

Most occurring characters

ValueCountFrequency (%)
5 1843
92.3%
6 154
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1843
92.3%
6 154
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1843
92.3%
6 154
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1843
92.3%
6 154
 
7.7%

i14
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
3
1612 
4
363 
2
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1612
80.7%
4 363
 
18.2%
2 22
 
1.1%

Length

2023-11-28T10:07:12.604808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:12.998404image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
3 1612
80.7%
4 363
 
18.2%
2 22
 
1.1%

Most occurring characters

ValueCountFrequency (%)
3 1612
80.7%
4 363
 
18.2%
2 22
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1612
80.7%
4 363
 
18.2%
2 22
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1612
80.7%
4 363
 
18.2%
2 22
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1612
80.7%
4 363
 
18.2%
2 22
 
1.1%

i15
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
5
1012 
6
836 
4
 
81
7
 
68

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row6
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
5 1012
50.7%
6 836
41.9%
4 81
 
4.1%
7 68
 
3.4%

Length

2023-11-28T10:07:13.468178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:13.878078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
5 1012
50.7%
6 836
41.9%
4 81
 
4.1%
7 68
 
3.4%

Most occurring characters

ValueCountFrequency (%)
5 1012
50.7%
6 836
41.9%
4 81
 
4.1%
7 68
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1012
50.7%
6 836
41.9%
4 81
 
4.1%
7 68
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1012
50.7%
6 836
41.9%
4 81
 
4.1%
7 68
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1012
50.7%
6 836
41.9%
4 81
 
4.1%
7 68
 
3.4%

i16
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
6
979 
7
472 
5
352 
8
152 
4
 
42

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row7
3rd row6
4th row7
5th row7

Common Values

ValueCountFrequency (%)
6 979
49.0%
7 472
23.6%
5 352
 
17.6%
8 152
 
7.6%
4 42
 
2.1%

Length

2023-11-28T10:07:14.336368image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:14.740755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
6 979
49.0%
7 472
23.6%
5 352
 
17.6%
8 152
 
7.6%
4 42
 
2.1%

Most occurring characters

ValueCountFrequency (%)
6 979
49.0%
7 472
23.6%
5 352
 
17.6%
8 152
 
7.6%
4 42
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 979
49.0%
7 472
23.6%
5 352
 
17.6%
8 152
 
7.6%
4 42
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 979
49.0%
7 472
23.6%
5 352
 
17.6%
8 152
 
7.6%
4 42
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 979
49.0%
7 472
23.6%
5 352
 
17.6%
8 152
 
7.6%
4 42
 
2.1%

i17
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
3
1035 
4
957 
5
 
3
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
3 1035
51.8%
4 957
47.9%
5 3
 
0.2%
2 2
 
0.1%

Length

2023-11-28T10:07:15.216216image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:15.614946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
3 1035
51.8%
4 957
47.9%
5 3
 
0.2%
2 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 1035
51.8%
4 957
47.9%
5 3
 
0.2%
2 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1035
51.8%
4 957
47.9%
5 3
 
0.2%
2 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1035
51.8%
4 957
47.9%
5 3
 
0.2%
2 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1035
51.8%
4 957
47.9%
5 3
 
0.2%
2 2
 
0.1%

i18
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
5
1423 
6
407 
4
167 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row6
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 1423
71.3%
6 407
 
20.4%
4 167
 
8.4%

Length

2023-11-28T10:07:16.094765image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:16.513782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
5 1423
71.3%
6 407
 
20.4%
4 167
 
8.4%

Most occurring characters

ValueCountFrequency (%)
5 1423
71.3%
6 407
 
20.4%
4 167
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1423
71.3%
6 407
 
20.4%
4 167
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1423
71.3%
6 407
 
20.4%
4 167
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1423
71.3%
6 407
 
20.4%
4 167
 
8.4%

i19
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:16.965695image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:17.352782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

i20
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:17.785218image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:18.177268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

i21
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:18.612248image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:18.995171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

i22
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:19.423563image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:19.803088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

i23
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:20.226361image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:20.607595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

i24
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:21.023246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:21.407102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

i25
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:21.834572image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:22.219727image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

i26
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:22.642645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:23.023193image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

i27
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:23.452731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:23.846384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

i28
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:24.284462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:24.660759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

i29
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:25.082753image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:25.464433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

i30
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:25.892344image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:26.293473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

i31
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:26.720865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:27.099346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

i32
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:27.524742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:27.910319image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

pf1
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.390085
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2023-11-28T10:07:28.367035image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median20
Q364
95-th percentile100
Maximum100
Range80
Interquartile range (IQR)44

Descriptive statistics

Standard deviation29.689923
Coefficient of variation (CV)0.68425594
Kurtosis-0.71770829
Mean43.390085
Median Absolute Deviation (MAD)0
Skewness0.88292013
Sum86650
Variance881.49154
MonotonicityNot monotonic
2023-11-28T10:07:28.961340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 1026
51.4%
100 222
 
11.1%
99 76
 
3.8%
64 46
 
2.3%
65 44
 
2.2%
63 43
 
2.2%
66 29
 
1.5%
62 28
 
1.4%
60 20
 
1.0%
67 20
 
1.0%
Other values (64) 443
22.2%
ValueCountFrequency (%)
20 1026
51.4%
21 8
 
0.4%
22 8
 
0.4%
23 6
 
0.3%
24 6
 
0.3%
25 7
 
0.4%
26 3
 
0.2%
27 5
 
0.3%
28 7
 
0.4%
29 7
 
0.4%
ValueCountFrequency (%)
100 222
11.1%
99 76
 
3.8%
98 4
 
0.2%
97 2
 
0.1%
94 1
 
0.1%
93 1
 
0.1%
92 2
 
0.1%
91 1
 
0.1%
89 2
 
0.1%
84 4
 
0.2%

pf2
Real number (ℝ)

Distinct26
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.023035
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2023-11-28T10:07:29.469431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median20
Q320
95-th percentile24.2
Maximum100
Range80
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.692931
Coefficient of variation (CV)0.63818396
Kurtosis22.647488
Mean23.023035
Median Absolute Deviation (MAD)0
Skewness4.9268557
Sum45977
Variance215.88223
MonotonicityNot monotonic
2023-11-28T10:07:30.007344image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
20 1893
94.8%
100 51
 
2.6%
99 16
 
0.8%
33 4
 
0.2%
40 3
 
0.2%
31 3
 
0.2%
41 2
 
0.1%
35 2
 
0.1%
29 2
 
0.1%
26 2
 
0.1%
Other values (16) 19
 
1.0%
ValueCountFrequency (%)
20 1893
94.8%
22 2
 
0.1%
24 2
 
0.1%
25 1
 
0.1%
26 2
 
0.1%
27 1
 
0.1%
28 1
 
0.1%
29 2
 
0.1%
31 3
 
0.2%
32 1
 
0.1%
ValueCountFrequency (%)
100 51
2.6%
99 16
 
0.8%
92 1
 
0.1%
90 1
 
0.1%
78 1
 
0.1%
55 1
 
0.1%
50 1
 
0.1%
48 1
 
0.1%
46 1
 
0.1%
43 1
 
0.1%

pf3
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
100
1995 
99
 
2

Length

Max length3
Median length3
Mean length2.9989985
Min length2

Characters and Unicode

Total characters5989
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 1995
99.9%
99 2
 
0.1%

Length

2023-11-28T10:07:30.535998image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:30.932640image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
100 1995
99.9%
99 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 3990
66.6%
1 1995
33.3%
9 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5989
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3990
66.6%
1 1995
33.3%
9 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5989
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3990
66.6%
1 1995
33.3%
9 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5989
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3990
66.6%
1 1995
33.3%
9 4
 
0.1%

pf4
Real number (ℝ)

Distinct39
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.95343
Minimum20
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2023-11-28T10:07:31.366807image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median20
Q320
95-th percentile23
Maximum85
Range65
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.3540327
Coefficient of variation (CV)0.25552058
Kurtosis61.758046
Mean20.95343
Median Absolute Deviation (MAD)0
Skewness7.3889223
Sum41844
Variance28.665666
MonotonicityNot monotonic
2023-11-28T10:07:31.908440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
20 1882
94.2%
21 10
 
0.5%
25 8
 
0.4%
32 7
 
0.4%
29 7
 
0.4%
23 6
 
0.3%
24 5
 
0.3%
31 5
 
0.3%
28 5
 
0.3%
22 4
 
0.2%
Other values (29) 58
 
2.9%
ValueCountFrequency (%)
20 1882
94.2%
21 10
 
0.5%
22 4
 
0.2%
23 6
 
0.3%
24 5
 
0.3%
25 8
 
0.4%
26 4
 
0.2%
27 3
 
0.2%
28 5
 
0.3%
29 7
 
0.4%
ValueCountFrequency (%)
85 1
 
0.1%
80 1
 
0.1%
79 1
 
0.1%
77 1
 
0.1%
75 1
 
0.1%
72 2
0.1%
69 1
 
0.1%
61 1
 
0.1%
60 1
 
0.1%
59 4
0.2%

pf5
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
100
1995 
99
 
2

Length

Max length3
Median length3
Mean length2.9989985
Min length2

Characters and Unicode

Total characters5989
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 1995
99.9%
99 2
 
0.1%

Length

2023-11-28T10:07:32.433095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:32.830607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
100 1995
99.9%
99 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 3990
66.6%
1 1995
33.3%
9 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5989
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3990
66.6%
1 1995
33.3%
9 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5989
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3990
66.6%
1 1995
33.3%
9 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5989
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3990
66.6%
1 1995
33.3%
9 4
 
0.1%

pf6
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
100
1933 
99
 
62
98
 
1
96
 
1

Length

Max length3
Median length3
Mean length2.9679519
Min length2

Characters and Unicode

Total characters5927
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 1933
96.8%
99 62
 
3.1%
98 1
 
0.1%
96 1
 
0.1%

Length

2023-11-28T10:07:33.282528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:33.707035image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
100 1933
96.8%
99 62
 
3.1%
98 1
 
0.1%
96 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 3866
65.2%
1 1933
32.6%
9 126
 
2.1%
8 1
 
< 0.1%
6 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5927
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3866
65.2%
1 1933
32.6%
9 126
 
2.1%
8 1
 
< 0.1%
6 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5927
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3866
65.2%
1 1933
32.6%
9 126
 
2.1%
8 1
 
< 0.1%
6 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3866
65.2%
1 1933
32.6%
9 126
 
2.1%
8 1
 
< 0.1%
6 1
 
< 0.1%

pf7
Real number (ℝ)

Distinct48
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.477216
Minimum20
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2023-11-28T10:07:33.988773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median20
Q320
95-th percentile31
Maximum84
Range64
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.992322
Coefficient of variation (CV)0.27900832
Kurtosis32.341298
Mean21.477216
Median Absolute Deviation (MAD)0
Skewness5.3020056
Sum42890
Variance35.907923
MonotonicityNot monotonic
2023-11-28T10:07:34.315013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
20 1790
89.6%
21 20
 
1.0%
23 15
 
0.8%
22 12
 
0.6%
28 11
 
0.6%
25 10
 
0.5%
24 10
 
0.5%
26 9
 
0.5%
30 8
 
0.4%
34 7
 
0.4%
Other values (38) 105
 
5.3%
ValueCountFrequency (%)
20 1790
89.6%
21 20
 
1.0%
22 12
 
0.6%
23 15
 
0.8%
24 10
 
0.5%
25 10
 
0.5%
26 9
 
0.5%
27 7
 
0.4%
28 11
 
0.6%
29 3
 
0.2%
ValueCountFrequency (%)
84 1
 
0.1%
76 1
 
0.1%
72 1
 
0.1%
71 2
0.1%
70 1
 
0.1%
65 1
 
0.1%
62 1
 
0.1%
61 1
 
0.1%
60 2
0.1%
58 4
0.2%

pf8
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
100
1996 
99
 
1

Length

Max length3
Median length3
Mean length2.9994992
Min length2

Characters and Unicode

Total characters5990
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 1996
99.9%
99 1
 
0.1%

Length

2023-11-28T10:07:34.778981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:35.179743image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
100 1996
99.9%
99 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 3992
66.6%
1 1996
33.3%
9 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5990
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3992
66.6%
1 1996
33.3%
9 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5990
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3992
66.6%
1 1996
33.3%
9 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3992
66.6%
1 1996
33.3%
9 2
 
< 0.1%

pf9
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
100
1986 
99
 
11

Length

Max length3
Median length3
Mean length2.9944917
Min length2

Characters and Unicode

Total characters5980
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 1986
99.4%
99 11
 
0.6%

Length

2023-11-28T10:07:35.638631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:36.049978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
100 1986
99.4%
99 11
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 3972
66.4%
1 1986
33.2%
9 22
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5980
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3972
66.4%
1 1986
33.2%
9 22
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 5980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3972
66.4%
1 1986
33.2%
9 22
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3972
66.4%
1 1986
33.2%
9 22
 
0.4%

pf10
Real number (ℝ)

Distinct61
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.927892
Minimum20
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2023-11-28T10:07:36.345598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median20
Q320
95-th percentile43
Maximum86
Range66
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.3854132
Coefficient of variation (CV)0.40934479
Kurtosis16.135443
Mean22.927892
Median Absolute Deviation (MAD)0
Skewness3.9108955
Sum45787
Variance88.08598
MonotonicityNot monotonic
2023-11-28T10:07:36.677509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 1709
85.6%
25 15
 
0.8%
23 14
 
0.7%
22 13
 
0.7%
32 13
 
0.7%
34 12
 
0.6%
30 11
 
0.6%
36 11
 
0.6%
33 11
 
0.6%
28 10
 
0.5%
Other values (51) 178
 
8.9%
ValueCountFrequency (%)
20 1709
85.6%
21 10
 
0.5%
22 13
 
0.7%
23 14
 
0.7%
24 7
 
0.4%
25 15
 
0.8%
26 4
 
0.2%
27 7
 
0.4%
28 10
 
0.5%
29 5
 
0.3%
ValueCountFrequency (%)
86 1
 
0.1%
85 1
 
0.1%
84 1
 
0.1%
82 1
 
0.1%
81 2
0.1%
78 3
0.2%
76 1
 
0.1%
75 4
0.2%
74 1
 
0.1%
73 3
0.2%

pf11
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
100
1996 
99
 
1

Length

Max length3
Median length3
Mean length2.9994992
Min length2

Characters and Unicode

Total characters5990
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 1996
99.9%
99 1
 
0.1%

Length

2023-11-28T10:07:36.977443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:37.202022image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
100 1996
99.9%
99 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 3992
66.6%
1 1996
33.3%
9 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5990
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3992
66.6%
1 1996
33.3%
9 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5990
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3992
66.6%
1 1996
33.3%
9 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3992
66.6%
1 1996
33.3%
9 2
 
< 0.1%

pf12
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
100
1542 
99
455 

Length

Max length3
Median length3
Mean length2.7721582
Min length2

Characters and Unicode

Total characters5536
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row99
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 1542
77.2%
99 455
 
22.8%

Length

2023-11-28T10:07:37.446576image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:37.674638image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
100 1542
77.2%
99 455
 
22.8%

Most occurring characters

ValueCountFrequency (%)
0 3084
55.7%
1 1542
27.9%
9 910
 
16.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5536
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3084
55.7%
1 1542
27.9%
9 910
 
16.4%

Most occurring scripts

ValueCountFrequency (%)
Common 5536
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3084
55.7%
1 1542
27.9%
9 910
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3084
55.7%
1 1542
27.9%
9 910
 
16.4%

pf13
Real number (ℝ)

SKEWED 

Distinct13
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.140711
Minimum20
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2023-11-28T10:07:37.878484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median20
Q320
95-th percentile20
Maximum78
Range58
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.241155
Coefficient of variation (CV)0.11127487
Kurtosis475.90944
Mean20.140711
Median Absolute Deviation (MAD)0
Skewness20.639452
Sum40221
Variance5.0227756
MonotonicityNot monotonic
2023-11-28T10:07:38.121381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
20 1984
99.3%
29 2
 
0.1%
78 1
 
0.1%
26 1
 
0.1%
31 1
 
0.1%
36 1
 
0.1%
25 1
 
0.1%
51 1
 
0.1%
52 1
 
0.1%
37 1
 
0.1%
Other values (3) 3
 
0.2%
ValueCountFrequency (%)
20 1984
99.3%
25 1
 
0.1%
26 1
 
0.1%
27 1
 
0.1%
29 2
 
0.1%
31 1
 
0.1%
36 1
 
0.1%
37 1
 
0.1%
43 1
 
0.1%
51 1
 
0.1%
ValueCountFrequency (%)
78 1
0.1%
77 1
0.1%
52 1
0.1%
51 1
0.1%
43 1
0.1%
37 1
0.1%
36 1
0.1%
31 1
0.1%
29 2
0.1%
27 1
0.1%

pf14
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
100
1843 
99
 
154

Length

Max length3
Median length3
Mean length2.9228843
Min length2

Characters and Unicode

Total characters5837
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row99
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 1843
92.3%
99 154
 
7.7%

Length

2023-11-28T10:07:38.394449image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:38.636167image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
100 1843
92.3%
99 154
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 3686
63.1%
1 1843
31.6%
9 308
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5837
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3686
63.1%
1 1843
31.6%
9 308
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5837
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3686
63.1%
1 1843
31.6%
9 308
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5837
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3686
63.1%
1 1843
31.6%
9 308
 
5.3%

pf15
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
100
1982 
99
 
15

Length

Max length3
Median length3
Mean length2.9924887
Min length2

Characters and Unicode

Total characters5976
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 1982
99.2%
99 15
 
0.8%

Length

2023-11-28T10:07:39.091274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:39.511186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
100 1982
99.2%
99 15
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 3964
66.3%
1 1982
33.2%
9 30
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5976
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3964
66.3%
1 1982
33.2%
9 30
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 5976
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3964
66.3%
1 1982
33.2%
9 30
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3964
66.3%
1 1982
33.2%
9 30
 
0.5%

pf16
Real number (ℝ)

Distinct40
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.741112
Minimum20
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2023-11-28T10:07:39.746475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median20
Q320
95-th percentile21
Maximum85
Range65
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.6086937
Coefficient of variation (CV)0.2222009
Kurtosis80.463821
Mean20.741112
Median Absolute Deviation (MAD)0
Skewness8.3731822
Sum41420
Variance21.240057
MonotonicityNot monotonic
2023-11-28T10:07:40.214871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
20 1895
94.9%
22 11
 
0.6%
21 10
 
0.5%
27 8
 
0.4%
23 7
 
0.4%
28 6
 
0.3%
43 4
 
0.2%
24 3
 
0.2%
30 3
 
0.2%
31 3
 
0.2%
Other values (30) 47
 
2.4%
ValueCountFrequency (%)
20 1895
94.9%
21 10
 
0.5%
22 11
 
0.6%
23 7
 
0.4%
24 3
 
0.2%
25 3
 
0.2%
26 3
 
0.2%
27 8
 
0.4%
28 6
 
0.3%
29 3
 
0.2%
ValueCountFrequency (%)
85 1
0.1%
78 1
0.1%
76 1
0.1%
72 1
0.1%
69 1
0.1%
65 1
0.1%
61 1
0.1%
60 1
0.1%
59 2
0.1%
58 1
0.1%

pf17
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
100
1997 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5991
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 1997
100.0%

Length

2023-11-28T10:07:40.729462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:41.115252image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
100 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3994
66.7%
1 1997
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5991
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3994
66.7%
1 1997
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5991
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3994
66.7%
1 1997
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5991
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3994
66.7%
1 1997
33.3%

pf18
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
100
1993 
99
 
4

Length

Max length3
Median length3
Mean length2.997997
Min length2

Characters and Unicode

Total characters5987
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 1993
99.8%
99 4
 
0.2%

Length

2023-11-28T10:07:41.578181image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:41.988449image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
100 1993
99.8%
99 4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 3986
66.6%
1 1993
33.3%
9 8
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5987
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3986
66.6%
1 1993
33.3%
9 8
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5987
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3986
66.6%
1 1993
33.3%
9 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5987
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3986
66.6%
1 1993
33.3%
9 8
 
0.1%

pf19
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:42.430792image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:42.813108image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

pf20
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:43.246041image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:43.614698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

pf21
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:43.850674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:44.062161image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

pf22
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:44.293642image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:44.494117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

pf23
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:44.895164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:45.284997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

pf24
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:45.713148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:46.113983image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

pf25
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:46.554008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:46.940621image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

pf26
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:47.372473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:47.765010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

pf27
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:48.204602image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:48.590923image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

pf28
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:49.036553image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:49.444986image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

pf29
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:49.892817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:50.293604image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

pf30
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:50.723128image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:51.118830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

pf31
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:51.546249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:51.936127image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

pf32
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1997
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
100.0%

Length

2023-11-28T10:07:52.378894image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T10:07:52.763169image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1997
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1997
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1997
100.0%

server_date_time
Date

UNIQUE 

Distinct1997
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
Minimum2023-10-12 19:04:00
Maximum2023-10-25 08:57:00
2023-11-28T10:07:53.225354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:07:53.802485image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-11-28T10:06:50.987578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:05:56.636254image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:05:59.581510image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:03.853336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:08.427351image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:13.210998image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:17.836210image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:22.679496image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:27.363396image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:32.196381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:36.883881image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:39.969620image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:42.965514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:47.405556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:51.206737image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:05:56.831293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:05:59.787723image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:04.057917image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:08.754920image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:13.533344image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:18.167371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:23.001764image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:27.696456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:32.528923image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:37.084637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:40.171838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:43.187698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:47.724953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:51.432536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:05:57.039584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:00.008085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:04.404535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:09.092535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:13.869233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:18.518584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:23.343404image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:28.049911image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:32.885467image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:37.311929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:40.396904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:43.431455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:48.072602image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:52.826361image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:05:57.242369image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:00.227411image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:04.733297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:09.426421image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:14.186184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:18.858530image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:23.670698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:28.390283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:33.229487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:37.525255image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:40.599782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:43.654378image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:48.411763image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:53.035384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:05:57.439597image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:01.407306image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:05.061895image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:09.760907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:14.505395image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:19.199392image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:23.999035image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:28.732912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:33.575628image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:37.731126image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:40.804430image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:43.947156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:48.750727image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:53.245820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:05:57.634346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:01.604351image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:05.387105image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:10.084006image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:14.806901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:19.521761image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:24.320447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:29.063808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:33.905981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:37.932422image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:41.001042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:44.276691image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:48.948496image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:53.480344image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:05:57.846329image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:01.829814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:05.725498image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:10.430871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:15.142720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:19.875504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:24.662325image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:29.414785image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:34.263377image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:38.159978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:41.235314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:44.636973image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:49.181214image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:53.693865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:05:58.053137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:02.033388image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:06.060894image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:10.765888image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:15.470675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:20.214246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:24.992406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:29.752981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:34.597319image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:38.379987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:41.441363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:44.951651image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:49.409674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:53.926346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:05:58.284761image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:02.391664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:06.413943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:11.118283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:15.812803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:20.571750image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:25.345255image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:30.120608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:34.958967image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:38.621190image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:41.664973image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:45.319424image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:49.648047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:54.160331image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:05:58.517927image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:02.751304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:06.763183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:11.469514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:16.177018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:20.935422image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:25.693175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:30.477563image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:35.327503image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:38.855780image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:41.888434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:45.682443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:49.884106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:54.391631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:05:58.738936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:02.968826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:07.093008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:11.806260image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:16.516226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:21.284166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:26.026498image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:30.818954image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:35.677154image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:39.079540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:42.102076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:46.033764image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:50.118619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:54.606151image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:05:58.946378image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:03.183129image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:07.424276image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:12.150747image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:16.844907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:21.630947image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:26.365772image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:31.165916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:36.030163image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:39.315937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:42.314017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:46.386139image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:50.345387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:54.823275image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:05:59.157703image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:03.402894image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:07.754978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:12.505630image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:17.174543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:21.989478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:26.700950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:31.510040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:36.375388image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:39.530242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:42.531493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:46.728527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:50.556562image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:55.044560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:05:59.366879image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:03.629994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:08.084438image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:12.863871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:17.501634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:22.333988image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:27.029947image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:31.847725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:36.659806image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:39.739658image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:42.748466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:47.065455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-28T10:06:50.775833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-28T10:07:54.256321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
idv_redv_bluev_yellowi1i2i6pf1pf2pf4pf7pf10pf13pf16i3i4i5i7i8i9i10i11i12i13i14i15i16i17i18pf3pf5pf6pf8pf9pf11pf12pf14pf15pf18
id1.000-0.257-0.340-0.340-0.019-0.226-0.387-0.250-0.012-0.1180.0140.0150.0140.0300.2840.3270.3930.3540.3520.2520.3180.2980.4420.3550.4680.4240.4140.3580.4710.0710.0710.1340.0150.1050.0000.2640.2840.0770.024
v_red-0.2571.0000.1020.102-0.0720.2150.861-0.104-0.1720.2330.004-0.074-0.0130.0480.2840.3250.2940.3120.3830.2520.3480.2160.2800.3420.3370.3250.3140.2920.3950.0000.0000.1260.0000.0510.0520.2120.2420.0360.000
v_blue-0.3400.1021.0001.0000.0850.0910.2260.110-0.006-0.0050.011-0.0090.0250.0170.1900.1470.1130.2050.1930.2200.1670.2540.1330.2340.1490.2280.2050.0000.2130.2630.4310.0050.1050.3600.5740.1810.3290.2460.331
v_yellow-0.3400.1021.0001.0000.0850.0910.2260.110-0.006-0.0050.011-0.0090.0250.0170.1900.1470.1130.2050.1930.2200.1670.2540.1330.2340.1490.2280.2050.0000.2130.2630.4310.0050.1050.3600.5740.1810.3290.2460.331
i1-0.019-0.0720.0850.0851.000-0.1450.1060.873-0.058-0.060-0.0530.208-0.017-0.1480.1880.1660.2530.2620.1890.1840.1620.5060.5590.5750.5890.3860.3730.2890.5230.0000.0000.0000.0000.0000.0000.1670.0840.0000.000
i2-0.2260.2150.0910.091-0.1451.0000.114-0.083-0.3180.1030.091-0.0530.0370.0310.2040.2900.2750.3100.2560.2120.2820.2250.3660.2890.3580.3050.3150.2300.4090.0000.0000.0420.0000.0730.0000.1070.1540.0000.000
i6-0.3870.8610.2260.2260.1060.1141.0000.083-0.0890.242-0.026-0.030-0.0050.0100.3550.3490.6060.2990.3520.2430.3030.1450.2530.1880.2320.2630.2890.1950.4020.0870.0190.1230.0510.0180.0000.1930.2370.0720.000
pf1-0.250-0.1040.1100.1100.873-0.0830.0831.0000.039-0.037-0.0490.1780.036-0.1560.2550.2960.2960.3680.3170.2600.3100.4240.5950.5230.6150.5220.4590.4580.5830.0000.0000.1530.0000.0000.0000.2790.1960.0260.000
pf2-0.012-0.172-0.006-0.006-0.058-0.318-0.0890.0391.000-0.018-0.072-0.0040.093-0.0130.0900.1020.0920.1940.1970.1360.0650.2070.0690.0870.0280.1820.1070.1310.0620.0000.0000.0000.0000.0000.0000.0620.0520.0000.000
pf4-0.1180.233-0.005-0.005-0.0600.1030.242-0.037-0.0181.0000.0360.012-0.0200.0730.0000.0090.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0290.0000.0000.0000.0000.0420.0000.000
pf70.0140.0040.0110.011-0.0530.091-0.026-0.049-0.0720.0361.000-0.003-0.0270.1190.0000.0000.0390.0000.0460.0000.0540.0000.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.1330.0000.0210.0000.0000.000
pf100.015-0.074-0.009-0.0090.208-0.053-0.0300.178-0.0040.012-0.0031.000-0.017-0.0230.0440.0960.0650.1060.0740.0470.0770.0000.0430.0000.0000.1100.0830.1080.0760.0000.0000.0000.0000.0000.0000.0760.0410.0000.000
pf130.014-0.0130.0250.025-0.0170.037-0.0050.0360.093-0.020-0.027-0.0171.000-0.0190.0000.0000.0000.0600.0000.0910.0000.1240.0000.0940.0000.0730.0270.2310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
pf160.0300.0480.0170.017-0.1480.0310.010-0.156-0.0130.0730.119-0.023-0.0191.0000.0510.0530.0540.0750.0150.0180.0330.0000.0000.0000.0000.0370.0460.0440.0000.0000.0000.0000.0000.0700.0000.0000.0000.0610.000
i30.2840.2840.1900.1900.1880.2040.3550.2550.0900.0000.0000.0440.0000.0511.0000.5310.4000.4670.5530.4270.4800.1720.2750.1200.2840.3790.4270.1920.5710.0180.0460.0300.0770.0000.0000.3010.3780.0310.000
i40.3270.3250.1470.1470.1660.2900.3490.2960.1020.0090.0000.0960.0000.0530.5311.0000.6400.5580.5310.3920.5820.2680.2360.2390.3220.5020.4680.4250.5060.1320.1320.0500.0000.0200.0000.3370.4040.0470.087
i50.3930.2940.1130.1130.2530.2750.6060.2960.0920.0000.0390.0650.0000.0540.4000.6401.0000.4780.4300.4160.4630.2750.3670.2440.2450.3830.3740.3800.4340.0280.0280.0320.0000.0000.0000.2920.2930.0700.000
i70.3540.3120.2050.2050.2620.3100.2990.3680.1940.0000.0000.1060.0600.0750.4670.5580.4781.0000.5460.5250.4930.5410.3310.3840.4410.6160.6020.4050.5320.0760.0760.0590.0000.0550.0000.3800.4360.0770.037
i80.3520.3830.1930.1930.1890.2560.3520.3170.1970.0280.0460.0740.0000.0150.5530.5310.4300.5461.0000.6550.6910.4060.1810.2750.2600.5570.5920.3600.5810.0780.0780.0590.0510.0440.0000.3240.4110.0460.047
i90.2520.2520.2200.2200.1840.2120.2430.2600.1360.0000.0000.0470.0910.0180.4270.3920.4160.5250.6551.0000.5410.5240.5520.3680.2880.4210.4810.2890.5350.0640.0640.0730.0360.0000.0000.3240.3910.2590.019
i100.3180.3480.1670.1670.1620.2820.3030.3100.0650.0000.0540.0770.0000.0330.4800.5820.4630.4930.6910.5411.0000.3260.3220.3040.3480.5300.4360.4170.6590.0830.0830.0640.0490.0220.0000.3590.4280.1240.046
i110.2980.2160.2540.2540.5060.2250.1450.4240.2070.0000.0000.0000.1240.0000.1720.2680.2750.5410.4060.5240.3261.0000.3950.5880.4310.7290.5620.2730.3830.0000.0000.0000.0000.0000.0000.0570.0400.0000.000
i120.4420.2800.1330.1330.5590.3660.2530.5950.0690.0000.0000.0430.0000.0000.2750.2360.3670.3310.1810.5520.3220.3951.0000.5230.5980.4610.5890.4890.6110.0000.0000.0480.0000.0000.0000.2300.1300.1810.000
i130.3550.3420.2340.2340.5750.2890.1880.5230.0870.0000.0000.0000.0940.0000.1200.2390.2440.3840.2750.3680.3040.5880.5231.0000.6080.5660.6300.3190.5330.0000.0000.0000.0000.0000.0000.1080.0620.0000.000
i140.4680.3370.1490.1490.5890.3580.2320.6150.0280.0000.0000.0000.0000.0000.2840.3220.2450.4410.2600.2880.3480.4310.5980.6081.0000.5550.6600.4080.6570.0000.0000.0000.0000.0180.0000.2420.2740.0350.098
i150.4240.3250.2280.2280.3860.3050.2630.5220.1820.0000.0370.1100.0730.0370.3790.5020.3830.6160.5570.4210.5300.7290.4610.5660.5551.0000.6800.5320.6320.1490.0650.0280.1020.0580.0000.3560.4200.0610.113
i160.4140.3140.2050.2050.3730.3150.2890.4590.1070.0000.0000.0830.0270.0460.4270.4680.3740.6020.5920.4810.4360.5620.5890.6300.6600.6801.0000.4440.7200.2110.1020.0360.0180.0590.0000.4020.4580.0760.068
i170.3580.2920.0000.0000.2890.2300.1950.4580.1310.0000.0000.1080.2310.0440.1920.4250.3800.4050.3600.2890.4170.2730.4890.3190.4080.5320.4441.0000.4010.0000.0000.0270.0000.0430.0000.3050.2190.0000.000
i180.4710.3950.2130.2130.5230.4090.4020.5830.0620.0260.0000.0760.0000.0000.5710.5060.4340.5320.5810.5350.6590.3830.6110.5330.6570.6320.7200.4011.0000.1000.0370.0220.0670.0500.0000.3350.4190.0480.062
pf30.0710.0000.2630.2630.0000.0000.0870.0000.0000.0000.0000.0000.0000.0000.0180.1320.0280.0760.0780.0640.0830.0000.0000.0000.0000.1490.2110.0000.1001.0000.2480.0000.0000.1020.0000.0000.0770.0000.000
pf50.0710.0000.4310.4310.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0460.1320.0280.0760.0780.0640.0830.0000.0000.0000.0000.0650.1020.0000.0370.2481.0000.0000.0000.0000.0000.0320.0770.0000.000
pf60.1340.1260.0050.0050.0000.0420.1230.1530.0000.0290.0000.0000.0000.0000.0300.0500.0320.0590.0590.0730.0640.0000.0480.0000.0000.0280.0360.0270.0220.0000.0001.0000.1190.0000.0000.0320.0420.0340.000
pf80.0150.0000.1050.1050.0000.0000.0510.0000.0000.0000.0000.0000.0000.0000.0770.0000.0000.0000.0510.0360.0490.0000.0000.0000.0000.1020.0180.0000.0670.0000.0000.1191.0000.0000.0000.0000.0280.0000.000
pf90.1050.0510.3600.3600.0000.0730.0180.0000.0000.0000.1330.0000.0000.0700.0000.0200.0000.0550.0440.0000.0220.0000.0000.0000.0180.0580.0590.0430.0500.1020.0000.0000.0001.0000.0000.0000.0630.0000.000
pf110.0000.0520.5740.5740.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0280.1260.000
pf120.2640.2120.1810.1810.1670.1070.1930.2790.0620.0000.0210.0760.0000.0000.3010.3370.2920.3800.3240.3240.3590.0570.2300.1080.2420.3560.4020.3050.3350.0000.0320.0320.0000.0000.0001.0000.2020.0810.000
pf140.2840.2420.3290.3290.0840.1540.2370.1960.0520.0420.0000.0410.0000.0000.3780.4040.2930.4360.4110.3910.4280.0400.1300.0620.2740.4200.4580.2190.4190.0770.0770.0420.0280.0630.0280.2021.0000.0690.045
pf150.0770.0360.2460.2460.0000.0000.0720.0260.0000.0000.0000.0000.0000.0610.0310.0470.0700.0770.0460.2590.1240.0000.1810.0000.0350.0610.0760.0000.0480.0000.0000.0340.0000.0000.1260.0810.0691.0000.057
pf180.0240.0000.3310.3310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0870.0000.0370.0470.0190.0460.0000.0000.0000.0980.1130.0680.0000.0620.0000.0000.0000.0000.0000.0000.0000.0450.0571.000

Missing values

2023-11-28T10:06:55.668159image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-28T10:06:56.512095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idmoduleidpacket_date_timev_redv_bluev_yellowi1i2i3i4i5i6i7i8i9i10i11i12i13i14i15i16i17i18i19i20i21i22i23i24i25i26i27i28i29i30i31i32pf1pf2pf3pf4pf5pf6pf7pf8pf9pf10pf11pf12pf13pf14pf15pf16pf17pf18pf19pf20pf21pf22pf23pf24pf25pf26pf27pf28pf29pf30pf31pf32server_date_time
2368170496LBTR07DB0111/11/2011 11:112043766384268779556731534434000000000000002020100201001002010010020100992099100201001000000000000000010/13/2023 12:27
2369175104LBTR07DB0111/11/2011 11:1114249112264339889767932646746000000000000001002010020100100201001002010010020100100201001000000000000000010/17/2023 2:23
2370176128LBTR07DB0111/11/2011 11:111202311652931098877793153664500000000000000582010020100100201001002010010020100100201001000000000000000010/19/2023 2:39
2371176384LBTR07DB0111/11/2011 11:111193311672501098877793153674500000000000000622010020100100201001004110010020100100201001000000000000000010/19/2023 5:46
2372181504LBTR07DB0111/11/2011 11:1182951137810987777931536745000000000000002010010020100100201001002010010020100100201001000000000000000010/21/2023 21:48
2373182784LBTR07DB0111/11/2011 11:111247911452631098876793153563500000000000000272010020100100201001002010010020100100201001000000000000000010/22/2023 4:40
2374184064LBTR07DB0111/11/2011 11:1117670113987298786678315355350000000000000020201002010010036100100201009920100100201001000000000000000010/22/2023 18:08
2375185856LBTR07DB0111/11/2011 11:11202671140433987965673153553400000000000000202010020100100201001002010010020100100201001000000000000000010/23/2023 14:45
2376191232LBTR07DB0111/11/2011 11:111052211436701098776793153563500000000000000202010020100100201001002010010020100100201001000000000000000010/25/2023 0:42
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